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Transportation Module of Global Change Assessment Model (GCAM): Model Documentation- Version 1.0

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  • Mishra, Gouri S.
  • Kyle, Page
  • Teter, Jacob
  • Morrison, Geoffrey M.
  • Kim, Son H.
  • Yeh, Sonia

Abstract

This publication provides methodological detail on the new GCAM Transportation Module and contains the following: (1) Descriptions of the new transportation module in GCAM (2) Details about the data sources and methodology adopted to estimate the exogeneous input parameters (3) A summary of the region-specific transportation data for base year (2005) (4) Comparisons of these estimates across regions and modes. (5) Highlights of the uncertainty and shortcomings in our estimates The project broadly encompasses the following four refinements to the transportation sector of GCAM: 1) Increased resolution to include the full spectrum of sub-modes and technologies available in passenger and frieght transport; 2) Refined estimates of input parameters so as to better represent real-world heterogeneity in a way consistent with the latest literature on transportation; 3) Refined estimates of base year (2005) estimates of transportation demand, and disaggregation of IEA energy estimates between modes and size classes; 4) Included the non-motorized modes of walking and biking. The above refinements will not only allow us to develop better estimates of transportation energy demand and emissions, but will also enable modeling of the impact of policies that induce behavioral change and switching to different size classes within a single fuel type. Existing literature on long-term forecasts of transportation energy demand and emissions have focused on the role of advanced low-emission vehicle technologies and low-carbon energy carriers in achieving climate change goals. In GCAM, modeling the impact of policies in the form of varying levels of carbon prices has, to date, been restricted to consumer choices for different modes (e.g. rail versus personal car) and different vehicle technologies (e.g. internal combustion engine vehicles versus electric vehicle). A more detailed representation of the transportation sector – including various size classes of vehicles -- will allow us to estimate the potential for downsizing in the case of private modes (large LDV to midsize or compact LDVs), transfer to public modes (rail and bus) or to non-motorized transport (walking and biking), and adoption of energy efficient “new” modes like the electric-bikes, which have seen rapid adoption in China and other developing countries. This project aims to better represent the heterogeneity and flexibility in the transport system to allow the modeling of a broader range of transport policy intruments including subsidies to public transit, government incentives for alternative technology, transportation fuel taxes, and public investments to increase the speed, service frequency/availability, and comfort of public and nonmotorized modes.

Suggested Citation

  • Mishra, Gouri S. & Kyle, Page & Teter, Jacob & Morrison, Geoffrey M. & Kim, Son H. & Yeh, Sonia, 2013. "Transportation Module of Global Change Assessment Model (GCAM): Model Documentation- Version 1.0," Institute of Transportation Studies, Working Paper Series qt8nk2c96d, Institute of Transportation Studies, UC Davis.
    • Handle: RePEc:cdl:itsdav:qt8nk2c96d
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    References listed on IDEAS

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    1. Campos, Javier & de Rus, Ginés, 2009. "Some stylized facts about high-speed rail: A review of HSR experiences around the world," Transport Policy, Elsevier, vol. 16(1), pages 19-28, January.
    2. José Carbajo & Antonio Estache, 1996. "Railway Concessions : Heading Down the Right Track in Argentina," World Bank Publications - Reports 11612, The World Bank Group.
    3. Eom, Jiyong & Schipper, Lee, 2010. "Trends in passenger transport energy use in South Korea," Energy Policy, Elsevier, vol. 38(7), pages 3598-3607, July.
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    Cited by:

    1. Dirk-Jan van de Ven & Mikel González-Eguino & Iñaki Arto, 2018. "The potential of behavioural change for climate change mitigation: a case study for the European Union," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 23(6), pages 853-886, August.
    2. Shuanghui Bao & Osamu Nishiura & Shinichiro Fujimori & Ken Oshiro & Runsen Zhang, 2020. "Identification of Key Factors to Reduce Transport-Related Air Pollutants and CO 2 Emissions in Asia," Sustainability, MDPI, vol. 12(18), pages 1-15, September.
    3. Runsen Zhang & Tatsuya Hanaoka, 2022. "Cross-cutting scenarios and strategies for designing decarbonization pathways in the transport sector toward carbon neutrality," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
    4. Paladugula, Anantha Lakshmi & Kholod, Nazar & Chaturvedi, Vaibhav & Ghosh, Probal Pratap & Pal, Sarbojit & Clarke, Leon & Evans, Meredydd & Kyle, Page & Koti, Poonam Nagar & Parikh, Kirit & Qamar, Sha, 2018. "A multi-model assessment of energy and emissions for India's transportation sector through 2050," Energy Policy, Elsevier, vol. 116(C), pages 10-18.
    5. Seungho Jeon & Minyoung Roh & Almas Heshmati & Suduk Kim, 2020. "An Assessment of Corporate Average Fuel Economy Standards for Passenger Cars in South Korea," Energies, MDPI, vol. 13(17), pages 1-13, September.
    6. Tianye Wang & Ekundayo Shittu, 2023. "Simulating the Impact of the U.S. Inflation Reduction Act on State-Level CO 2 Emissions: An Integrated Assessment Model Approach," Sustainability, MDPI, vol. 15(24), pages 1-16, December.
    7. Zhang, Hongjun & Chen, Wenying & Huang, Weilong, 2016. "TIMES modelling of transport sector in China and USA: Comparisons from a decarbonization perspective," Applied Energy, Elsevier, vol. 162(C), pages 1505-1514.
    8. Jha, Amit Prakash & Singh, Sanjay Kumar, 2022. "Future mobility in India from a changing energy mix perspective," Economic Analysis and Policy, Elsevier, vol. 73(C), pages 706-724.
    9. Durand-Lasserve, Olivier & Pierru, Axel, 2021. "Modeling world oil market questions: An economic perspective," Energy Policy, Elsevier, vol. 159(C).
    10. Yan, Shiyu & De Bruin, Kelly & Dennehy, Emer & Curtis, John, 2020. "A freight transport demand, energy and emission model with technological choices," Papers WP669, Economic and Social Research Institute (ESRI).

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